Suhani-2407/Fire-Detection

1from flask import Flask, request, jsonify2import tensorflow as tf3import numpy as np4from tensorflow.keras.preprocessing import image5 6app = Flask(__name__)7model = tf.keras.models.load_model("MobileNet_Fire.h5")8 9class_labels = {0: "Fake", 1: "Low", 2: "Medium", 3: "High"}  # Update as per your training10 11@app.route("/predict", methods=["POST"])12def predict():13    file = request.files["file"]14    img = image.load_img(file, target_size=(128, 128))15    img_array = image.img_to_array(img) / 255.016    img_array = np.expand_dims(img_array, axis=0)17 18    predictions = model.predict(img_array)19    predicted_class = class_labels[np.argmax(predictions)]20    confidence = float(np.max(predictions))21 22    return jsonify({"prediction": predicted_class, "confidence": confidence})23 24if __name__ == "__main__":25    app.run(debug=True)26